Covid - CA flexdashboard (documentation)

## ── Attaching packages ───────────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ ggplot2 3.3.2          ✓ purrr   0.3.4     
## ✓ tibble  3.0.2          ✓ dplyr   1.0.0.9000
## ✓ tidyr   1.1.0          ✓ stringr 1.4.0     
## ✓ readr   1.3.1          ✓ forcats 0.5.0

## ── Conflicts ──────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

Packages

Updates (version 0.1.3)

Wrangling steps have been moved into helpers.R script (includes both import and wrangle scripts from the code folder)
Changed contents of valueBox()s to sentence case
Updated global maps with orthographic and Mercator
Moved worldwide cases to page 2, and US data to page 3

Johns Hopkins University’s CSSE Data

The COVID-19 data objects are loaded below from various sources. Some of these are updated daily, others are pre-existing datasets for geographic locators or other variables to aid in visualizing/mapping.

Import time series data from Github

This imports data from the CSSEGISandData. These are in the csse_covid_19_time_series folder. These files are updated daily.

# fs::dir_ls("code/")
source("code/01.0-import-csse-time-series.R")

The following info comes from the README in the time series repo folder:

## Time series summary (csse_covid_19_time_series)

This folder contains daily time series summary tables, including confirmed, 
deaths and recovered. All data is read in from the daily case report. 
The time series tables are subject to be updated if inaccuracies are identified
in our historical data. The daily reports will not be adjusted in these 
instances to maintain a record of raw data.

Two time series tables are for the US confirmed cases and deaths, reported at
the county level. They are named `time_series_covid19_confirmed_US.csv`, 
`time_series_covid19_deaths_US.csv`, respectively.

Three time series tables are for the global confirmed cases, recovered cases 
and deaths. Australia, Canada and China are reported at the province/state 
level. Dependencies of the Netherlands, the UK, France and Denmark are listed 
under the province/state level. The US and other countries are at the country
level. The tables are renamed `time_series_covid19_confirmed_global.csv` and 
`time_series_covid19_deaths_global.csv`, and 
`time_series_covid19_recovered_global.csv`, respectively.

### Update frequency

Once a day around 23:59 (UTC).

### Deprecated warning

The files below were archived here, and will no longer be updated. With the 
release of the new data structure, we are updating our time series tables to
reflect these changes. 

Please reference  `time_series_covid19_confirmed_global.csv` and 
`time_series_covid19_deaths_global.csv` for the latest time series data.

`time_series_19-covid-Confirmed.csv`
`time_series_19-covid-Deaths.csv`
`time_series_19-covid-Recovered.csv`

Export the raw csse covid 19 time series data (global)

All of the raw data files are in this list

names(covid_raw_data_files)

#> [1] "GDPRaw"           "TSConfirmedRaw"   "TSConfirmedUSRaw" "TSDeathsRaw"     
#> [5] "TSDeathsUSRaw"    "TSRecoveredRaw"

I’ve dropped each datasets into a raw folder with a date stamp for safe keeping.

fs::dir_tree(paste0("data/raw/", 
                    base::noquote(lubridate::today())), 
             recurse = FALSE)

#> data/raw/2020-07-11
#> ├── 2020-07-11-Covus.csv
#> ├── 2020-07-11-DeathsMapCovus.csv
#> ├── 2020-07-11-GDPRaw.csv
#> ├── 2020-07-11-MapCovus.csv
#> ├── 2020-07-11-NYTCovState.csv
#> ├── 2020-07-11-PosMapCovus.csv
#> ├── 2020-07-11-TSConfirmedRaw.csv
#> ├── 2020-07-11-TSConfirmedUSRaw.csv
#> ├── 2020-07-11-TSDeathsRaw.csv
#> ├── 2020-07-11-TSDeathsUSRaw.csv
#> ├── 2020-07-11-TSRecoveredRaw.csv
#> ├── 2020-07-11-TidyCovCaseData.csv
#> ├── 2020-07-11-TidyCovDeathData.csv
#> └── 2020-07-11-TidyPosMapCovus.csv

Wrangling CSSE time series data

The code chunk below runs the script for the wrangling steps to create the data visualizations using the time series CSSE data.

# fs::dir_ls("code/")
source("code/02.0-wrangle-csse-time-series.R")

The following steps were taken to wrangle the time series data:

Convert wide to long (Confirmed, Recovered, Deaths)

first I converted TSConfirmedRaw dataset to long form, and converts the Date variable to mdy()

Create WorldTSDataAll by joining Confirmed, Recovered, Deaths

This joins the Confirmed, Recovered, and Deaths together into WorldTSDataAll

USTSDataAll = join ConfirmedUS and DeathsUS

I want to mimic what I did with the WorldTSDataAll and join these two together. I want country_region to just be named country, and province_state to just be named state. Export these files to processed folder

Create SumRegionDate

this groups the WorldTSDataAll data by country_region and date, then summarizes the confirmed_sum, recovered_sum, and deaths_sum variables. Then it creates a “new case” column with dplyr::lag() with confirmed_sum and filters the date to max(date)
create a most recent day from SumRegionDate called recent_day

GDP Country Codes: create a smaller version of the GDPRaw dataset. I also rename some of the regions in Gdp2016

Gdp2016 %>% 
  dplyr::filter(str_detect(string = region, pattern = "Gambia, The"))

#> # A tibble: 1 x 4
#>   region      code   year country_region
#>   <chr>       <chr> <dbl> <chr>         
#> 1 Gambia, The GMB    2016 Gambia

CreateSumRegionDateCodes by joining SumRegionDate and Gdp2016

Join the SumRegionDate to the Gdp2016 data country. And because this is the first complete dataset I will be using for data visualizations, I will export this into the data/processed folder

fs::dir_ls(paste0("data/processed/", 
                    base::noquote(lubridate::today())),
           regexp = "SumRegionDate.csv")

#> data/processed/2020-07-11/2020-07-11-SumRegionDate.csv

Dashboard layout

This section covers the data visualizations in the dashboard. Because the dashboard is built using multiple pages, with orientation: columns and vertical_layout: fill. Read more about this layout here.

Page 1: Global COVID-19 Data

Page 1 is titled, Global COVID-19 Data

Column 1: `valueBox()`s (`.bg-secondary`)

Column {data-width=400 .bg-secondary}
-----------------------------------------------------------------------

These are built using the following valueBox()s. The dataset WorldTSRecent is below:

rmarkdown::paged_table(
head(WorldTSRecent)
)

This is a tibble with a single row for the most recent stats.

The valueBox() and prettyNum() functions display the objects below:

Worldwide confirmed cases as of…

WorldTSRecent$date

#> [1] "2020-07-10"

WorldTSRecent$confirmed_sum

#> [1] 12498467

### `r paste0("Confirmed Cases as of ", WorldTSRecent$date)` 

valueBox(prettyNum(WorldTSRecent$confirmed_sum, big.mark = ","), color = "tomato")

Worldwide recovered cases as of …

WorldTSRecent$date

#> [1] "2020-07-10"

WorldTSRecent$recovered_sum

#> [1] 6777981

### `r paste0("New Cases as of ", WorldTSRecent$date)`

valueBox(prettyNum(WorldTSRecent$`New Case`, big.mark = ","), color = "sandybrown")

Worldwide deaths as of…

WorldTSRecent$date

#> [1] "2020-07-10"

WorldTSRecent$deaths_sum

#> [1] 560209

### `r paste0("Worldwide deaths as of ", WorldTSRecent$date)` 

valueBox(prettyNum(WorldTSRecent$deaths_sum, big.mark = ","), color = "gray50")

Column 2: Worldwide COVID-19 Confirmed Cases (`.tabset`)

Column {data-width=600 .tabset}
-----------------------------------------------------------------------

This is the plotly::plot_geo() world map. The dataset that it requires is SumRegionDateCodes, and it’s available to view below:

rmarkdown::paged_table(
head(SumRegionDateCodes)
)

The visualization uses the plotly::plot_geo() function, which renders a full interactive globe!

# create recent_day
recent_day <- max(SumRegionDateCodes$date)
# Set country boundaries as light gray
line <- list(color = toRGB("#d1d1d1"), width = 0.2)

# create geo for map options
geo <- list(
  bgcolor = "#FFFAF0",
  showframe = FALSE,
  showcoastlines = FALSE,
  # this is the globe option
  projection = list(type = "orthographic"),
  resolution = "100",
  showcountries = TRUE,
  showocean = TRUE,
  showlakes = FALSE,
  showrivers = FALSE)

geo_map_confirm_cases <- plotly::plot_geo() %>%
  layout(
    geo = geo,
    paper_bgcolor = "#FFFAF0",
    title = paste0("World COVID-19 Confirmed Cases as of ", 
                   recent_day)) %>%
  add_trace(
    data = SumRegionDateCodes,
    z = ~Confirmed,
    color = ~Confirmed,
    colors = "Reds",
    text = ~country_region,
    locations = ~code,
    marker = list(line = line))

geo_map_confirm_cases

Column 3: Worldwide COVID-19 Deaths (`.tabset`)

This is a new Mercator map of the confirmed deaths from COVID.

# Set country boundaries as light gray
line <- list(color = toRGB("#d1d1d1"), width = 0.2)

# create geo for map options
# c("#B0E0E6", "#F0FFF0")
geo <- list(
  oceancolor = "#B0E0E6",
  showframe = FALSE,
  showcoastlines = FALSE,
  # this is the mercator option
  projection = list(type = 'Mercator'),
  resolution = "100",
  showcountries = TRUE,
  showocean = TRUE,
  showlakes = FALSE,
  showrivers = FALSE)

geo_map_deaths <- plotly::plot_geo() %>%
  layout(
    geo = geo,
    paper_bgcolor = "#e8f7fc",
    title = paste0("World COVID-19 Deaths as of ", 
                   recent_day)) %>%
  add_trace(
    data = SumRegionDateCodes,
    z = ~Deaths,
    color = ~Deaths,
    colors = "Greys",
    text = ~country_region,
    locations = ~code,
    marker = list(line = line))

geo_map_deaths

Column 4: Worldwide COVID-19 Cases (Cumulative) (`.tabset`)

Create WorldTSDataAllDate and WorldTSDataRecent

WorldTSDataAllDate is WorldTSDataAll grouped by date
This groups by the date column, the summarized the confirmed, deaths, and recovered

Create WorldTSDataAllDateLong from WorldTSDataAllDate

Now I restructure (pivot) to create WorldTSDataAllDateLong. These data are exported into the processed data folder

fs::dir_ls(paste0("data/processed/", 
                    base::noquote(lubridate::today())),
           regexp = "WorldTSDataAllDateLong.csv")

#> data/processed/2020-07-11/2020-07-11-WorldTSDataAllDateLong.csv

Now plot with the dataset WorldTSDataAllDateLong

# set colors
colors <- c("red1", "gray1", "springgreen2")
# font style
font_style <- list(
  family = "Ubuntu",
  size = 14,
  color = 'black')
# create base chart
world_cum_point_chart <- WorldTSDataAllDateLong %>%
  ggplot2::ggplot(aes(x = date,
                      y = cases,
                      color = status)) +
  geom_point(size = 1, alpha = 2/5) +
  scale_color_manual(values = colors) +
  scale_y_continuous(labels = scales::label_number_si(accuracy = 0.1)) +
    theme(
    plot.margin = margin(0, 0, 0, 0, "pt"),
    panel.background = element_rect(fill = "White"),
    legend.position = "left",
    axis.title = element_blank(),
    axis.ticks = element_blank()) +
  hrbrthemes::theme_ipsum_tw(plot_title_family = "Ubuntu") +
  labs(title = "Worldwide COVID-19 Cumulative Cases",
       y = "Cases",
       x = "Date",
       color = " ")
# pass over to plotly
ggplotly(world_cum_point_chart) %>%
  plotly::layout(legend = list(orientation = "h"),
                 font = font_style)

Column 4: Worldwide COVID-19 Dataset (`.tabset`)

This is a display of the data used in the maps.

This creates the searchable data table (DT::datatable).

SumRegionDateCodes %>%
  dplyr::select(`Country region` = country_region,
          `Country code` = code,
          Date = date,
          Confirmed,
          `New Cases`,
          Recovered,
          Deaths) %>%
  dplyr::arrange(desc(Confirmed)) %>%
  DT::datatable(
    rownames = FALSE,
    fillContainer = TRUE,
    options = list(
      bPaginate = FALSE
    )
  )

Page 2: Daily Worldwide COVID-19 Cases

This page is titled, “Daily Worldwide COVID-19 Cases”

Column 1: `valueBox()`s (`.bg-secondary`)

New worldwide COVID-19 cases as of…

WorldTSRecent$`New Case`

#> [1] 229949

WorldTSRecent$date

#> [1] "2020-07-10"

### `r paste0("New worldwide COVID-19 cases as of ", WorldTSRecent$date)`

valueBox(prettyNum(WorldTSRecent$`New Case`, big.mark = ","), color = "sandybrown")

Days since first confirmed case at…

days_passed

#> Time difference of 171 days

case_no1

#> [1] "2020-01-22"

### `r paste0("Days since first confirmed case at ", case_no1)` 

valueBox(prettyNum(days_passed, big.mark = ","), color = "lightgoldenrodyellow")

Column 2: Daily Worldwide COVID-19 Cases (Animated)

Below we create two new graphs with gganimate using global and national data.

Create World data (animated) = WorldTSIncrementLong

This requires an incremental dataset that calculates new cases, deaths, and recovered patients with dplyr::lag()
status variable - dplyr::lag(status variable, 1)
Here we filter the WorldTSDataAll to only the US (WorldTSDataUS) and rename province_state and country_region
country_region == "US"

Create USA data (animated) = USTSDataAllIncrementLong

then I create an incremental dataset for US states by grouping by state, calculating the lag (between metric - dplyr::lag(metric)), then summarizing by date

Now we use the incremental dataset to animate the ggplot using gganimate

finally I build the animation, first with ggplot2, then pass the plot object to gganimate::transition_reveal(date) and assign the ggplot2::labs()

colors <- c("red1", "gray1", "springgreen2")
world_cum_cases <-
  WorldTSIncrementLong %>%

  ggplot2::ggplot(mapping = aes(x = date,
                  y = increment,
                  group = case,
                  color = case)) +
  ggplot2::geom_line(show.legend = FALSE,) +
  ggplot2::scale_y_continuous(labels = scales::label_number_si(accuracy = 1)) +
  ggplot2::scale_color_manual(values = colors) +
  ggplot2::geom_segment(aes(xend = max(date) + 1,
                            yend = increment),
                             linetype = 2,
                             colour = "grey1",
                             show.legend = FALSE) +
  ggplot2::geom_text(aes(x = max(date) + 1,
                label = case),
                 show.legend = FALSE,
                 hjust = 0) +
  hrbrthemes::theme_ipsum_tw(base_size = 10) +
  # set the coordinates
  ggplot2::coord_cartesian(
    xlim = c(min(WorldTSIncrementLong$date),
             max(WorldTSIncrementLong$date) + 7),
    ylim = c(max(0, min(WorldTSIncrementLong$increment)),
             max(WorldTSIncrementLong$increment)),
    clip = "off") +
  ggplot2::theme(legend.position = c(0.1, 0.8),
        axis.title.x = element_blank()) +
  ggplot2::guides(size = FALSE) +
  ggplot2::geom_point(aes(size = increment),
             alpha = 0.7,
             show.legend = FALSE) +

  ggplot2::scale_size(range = c(2, 10)) +

  gganimate::transition_reveal(date) +
  ggplot2::labs(title = "New Global COVID-19 Cases",
      subtitle = "Date: {frame_along}",
      y = "New daily cases",
      x = "Date")
animate_world_cum_cases <- gganimate::animate(world_cum_cases, nframes = 150,
                                              fps = 10,
                   renderer = gifski_renderer(loop = TRUE))

Now we animate and save.

# and save
gganimate::anim_save(filename =
                       base::paste0(base::noquote(lubridate::today()),
                                       "-animate_world_cum_cases.gif"),
                     animation = last_animation(),
                     path = "figs/")

knitr::include_graphics(path =
                          base::paste0("figs/",
                                       base::noquote(lubridate::today()),
                                                "-animate_world_cum_cases.gif"))

Page 3: United States COVID-19 Data

This page is the US time-series COVID-19 data.

Column 1: `valueBox()`s (`.bg-secondary`)

New cases as of

SumUSRecentCountry$date_max

#> [1] "2020-07-10"

SumUSRecentCountry$new_case_sum

#> [1] 66627

### `r paste0("New cases as of ", SumUSRecentCountry$date_max)` 

valueBox(prettyNum(SumUSRecentCountry$new_case_sum, big.mark = ","), color = "tomato")

Confirmed cases as of

SumUSRecentCountry$date_max

#> [1] "2020-07-10"

SumUSRecentCountry$confirmed_sum

#> [1] 3184573

### `r paste0("Confirmed cases as of ", SumUSRecentCountry$date_max)` 

valueBox(prettyNum(SumUSRecentCountry$confirmed_sum, big.mark = ","), color = "sandybrown")

Recovered cases as of

SumUSRecentCountry$date_max

#> [1] "2020-07-10"

SumUSRecentCountry$recovered_sum

#> [1] 983185

### `r paste0("Recovered cases as of ", SumUSRecentCountry$date_max)` 

valueBox(prettyNum(SumUSRecentCountry$recovered_sum, big.mark = ","), color = "palegreen")

Deaths as of

SumUSRecentCountry$date_max

#> [1] "2020-07-10"

SumUSRecentCountry$deaths_sum

#> [1] 134092

### `r paste0("Deaths as of ", SumUSRecentCountry$date_max)` 

valueBox(prettyNum(SumUSRecentCountry$deaths_sum, big.mark = ","), color = "gray50")

Days since first confirmed case on

us_first_case_day

#> [1] "2020-01-22"

us_days_passed

#> Time difference of 171 days

### `r paste0("Days since first confirmed case on ", us_first_case_day)` 

valueBox(prettyNum(us_days_passed, big.mark = ","), color = "#FFC300")

Column 2: United States Confirmed Cases (`.tabset`)

This is a map of the COVID-19 data for the US using the county-level data from the USTSDataAll dataset, but we need to reduce this to the 51 states in the continental US.

The data for these are stored in the confirmed_us vector.

head(confirmed_us, 10)

#>     Alabama      Alaska     Arizona    Arkansas  California    Colorado 
#>       50508        1323      116892       26803      310885       36173 
#> Connecticut    Delaware     Florida     Georgia 
#>       47287       12652      244151      111217

These are paired with the state.name and state.abb vectors from the maps package. Read more about how to create these in the plotly-r book.

us_map_layout <- list(
  scope = 'usa',
  lakecolor = "#3399FF",
  projection = list(type = 'albers usa'))

plot_geo() %>%
  add_trace(
    z = confirmed_us, 
    text = state.name, 
    span = I(0),
    locations = state.abb, 
    locationmode = 'USA-states') %>%
  layout(geo = us_map_layout,
         title = "Current US Confirmed Cases")

The same map is created below using the total confirmed deaths.

head(deaths_us, 10)

#>     Alabama      Alaska     Arizona    Arkansas  California    Colorado 
#>        1104          17        2082         313        6955        1724 
#> Connecticut    Delaware     Florida     Georgia 
#>        4348         517        4102        2965

United States Deaths

us_map_layout <- list(
  scope = 'usa',
  lakecolor = "#3399FF",
  projection = list(type = 'albers usa'))

plot_geo() %>%
  add_trace(
    z = deaths_us, 
    text = state.name, 
    span = I(0),
    locations = state.abb, 
    locationmode = 'USA-states') %>%
  layout(geo = us_map_layout,
         title = "Current US Deaths")

Column 3: United States COVID-19 Cases (Animated) (`.tabset`)

Now we can create the animated plot with ggplot2, specify the animation (gganimate::transition_reveal(date)), and pass it to the animation.

us_cum_cases <- USTSDataAllIncrementLong %>%
  ggplot2::ggplot(aes(x = date,
             y = increment,
             group = case,
             color = case)) +
    # add line
  ggplot2::geom_line(show.legend = FALSE) +
  ggplot2::scale_color_manual(values = c("red1", "black", "dodgerblue")) +
    # add segment, no legend
  ggplot2::geom_segment(aes(xend = max(date) + 1,
                            yend = increment),
                        linetype = 2,
                        color = "grey",
                        show.legend = FALSE) +
    # add text, no legend
  ggplot2::geom_text(aes(x = max(date) + 1,
                         label = case),
                     hjust = 0,
                     show.legend = FALSE) +
    # set theme
  hrbrthemes::theme_ipsum_tw(base_size = 10) +
    # set cartesian coordinates to min/max dates
  ggplot2::coord_cartesian(xlim = c(min(USTSDataAllIncrementLong$date),
                                    max(USTSDataAllIncrementLong$date) + 7),
                           clip = "off") +
    # position the legend
  ggplot2::theme(legend.position = c(0.1, 0.8),
                 # no x axis title
                 axis.title.x = element_blank()) +
    # no guides
  ggplot2::guides(size = FALSE) +
  ggplot2::geom_point(aes(size = increment),
                      alpha = 0.7,
                      show.legend = FALSE) +

  ggplot2::scale_size(range = c(2, 10)) +
    # set transition
  gganimate::transition_reveal(date) +
    # assign labs
  ggplot2::labs(title = "US COVID-19 Cases",
                subtitle = "at date: {frame_along}",
                y = "New Cases",
                color = "Status",
                x = "Date")

animated_us_cum_cases <- gganimate::animate(us_cum_cases,
                                            nframes = 150,
                                            fps = 10,
        renderer = gifski_renderer(loop = TRUE))

# and save
gganimate::anim_save(filename =
                       base::paste0(base::noquote(lubridate::today()),
                                       "-animated_us_cum_cases.gif"),
                     animation = last_animation(),
                     path = "figs/")

knitr::include_graphics(path = 
                          base::paste0("figs/", 
                                       base::noquote(lubridate::today()),
                                                "-animated_us_cum_cases.gif"))

Data Tables

These are the datasets currently displayed on last tab of the dashboard.

The US Data

Now we use the WorldTSDataUS for a searchable DT::datatable.

WorldTSDataUS %>%
  dplyr::select(Country = country,
         Date = date,
         Confirmed = confirmed,
         `New Case`,
         Recovered = recovered,
         Deaths = deaths) %>%
  dplyr::arrange(desc(Confirmed)) %>%
  DT::datatable(
    rownames = FALSE,
    fillContainer = TRUE,
    options = list(
      bPaginate = FALSE))

The `covdata` package

We’ll be using data from the covdata package by Kieran Healy. The goal with this package is to build an set of graphs that the user can select an input (selectInput()) from a list of metrics, and see that metric reflected across all 50 states.

The datasets we’ll be using are covus and nytcovstate. The script below imports and wrangles these data.

# fs::dir_ls("code")
source("code/01.1-import-wrangle-geofacet.R")

The `covus` data

This is a tidy dataset, with a date for each day, and each metric in the measure variable.

Covus %>% dplyr::glimpse()

#> Rows: 102,735
#> Columns: 16
#> $ date               <date> 2020-07-05, 2020-07-05, 2020-07-05, 2020-07-05,…
#> $ state              <chr> "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", …
#> $ fips               <chr> "02", "02", "02", "02", "02", "02", "02", "02", …
#> $ data_quality_grade <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A", "A"…
#> $ measure            <chr> "positive", "negative", "pending", "hospitalized…
#> $ count              <dbl> 1138, 122615, NA, 19, NA, NA, NA, 3, NA, 548, 16…
#> $ measure_label      <chr> "Positive Tests", "Negative Tests", "Pending Tes…
#> $ day                <int> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, …
#> $ yr                 <dbl> 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, …
#> $ week               <dbl> 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, …
#> $ week_year          <date> 2020-07-05, 2020-07-05, 2020-07-05, 2020-07-05,…
#> $ month_lbl          <ord> Jul, Jul, Jul, Jul, Jul, Jul, Jul, Jul, Jul, Jul…
#> $ month              <dbl> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, …
#> $ floor_month        <date> 2020-07-01, 2020-07-01, 2020-07-01, 2020-07-01,…
#> $ qtr                <int> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, …
#> $ days_elapsed       <drtn> 121 days, 121 days, 121 days, 121 days, 121 day…

A cleaner version of the measure variable is stored in the measure_label variable.

rmarkdown::paged_table(
dplyr::distinct(Covus, measure_label))

rmarkdown::paged_table(
dplyr::distinct(Covus, measure))

Now we add the necessary date variables grouped by state, remove the regions not included in the geofacet, and put these in a dataset called MapCovus.

rmarkdown::paged_table(
MapCovus %>% head())

rmarkdown::paged_table(
PosMapCovus %>% head())

rmarkdown::paged_table(
TidyPosMapCovus %>% head())

The `nytcovstate` data

rmarkdown::paged_table(
NYTCovState %>% head())

The `TidyCovDeathData` data

These data are used to compare deaths between NYT and COVID-tracking project.

rmarkdown::paged_table(
TidyCovDeathData %>% head())

Geofacets

We’ve covered how to create the geofacet graphs in this storybench post.

We will start by building a map with the positive tests in it (see the output below).

PosMapCovus %>% 
  dplyr::filter(state == "CA") %>% 
    ggplot2::ggplot(aes(x = days_elapsed, 
                        y = `positive tests`)) +
    geom_col(alpha = 1/10) + 
    ggplot2::labs(title = "California's new COVID cases", 
                  subtitle = paste0("Total COVID-19 positive tests between ",
                               min(PosMapCovus$date), 
                               " and ",
                               max(PosMapCovus$date)),
                  y = "Positive Tests", 
                  x = "Days") + 
  hrbrthemes::theme_ipsum_rc()

Test California with columns and lines.

# get California data
TidyPosMapCA <- TidyPosMapCovus %>% dplyr::filter(state == "CA")
# plot columns
ca_pos_col_plot <- PosMapCovus %>% 
  dplyr::filter(state == "CA") %>% 
    ggplot2::ggplot(aes(x = days_elapsed, 
                        y = `positive tests`,
                        group = date)) +
    geom_col(alpha = 1/10, linetype = 0, fill = "grey50") 

ca_pos_col_plot + 
  # add the lines 
    ggplot2::geom_line(data = TidyPosMapCA, 
                       mapping = aes(x = days_elapsed, 
                                     y = `Positive Test Value`, 
                                     group = `Positive Test Metric`,
                                     color = `Positive Test Metric`), 
                       show.legend = TRUE) +
    ggplot2::labs(title = "California's new COVID cases", 
                  subtitle = paste0("Total COVID-19 positive tests between ",
                               min(PosMapCovus$date), 
                               " and ",
                               max(PosMapCovus$date)),
                  y = "Positive Tests", 
                  x = "Days") + 
  hrbrthemes::theme_ipsum_rc()

US Positive Tests (COVID Tracking project)

Extend this to geofacet.

geofacet_pos <- PosMapCovus %>% 
    ggplot2::ggplot(aes(x = days_elapsed, 
                        y = `positive tests`,
                        group = date)) +
    geom_col(alpha = 2/10, 
             linetype = 0) + 
  
    ggplot2::geom_line(data = TidyPosMapCovus, 
                       mapping = aes(x = days_elapsed, 
                                     y = `Positive Test Value`, 
                                     group = `Positive Test Metric`,
                                     color = `Positive Test Metric`), 
                       show.legend = TRUE) +
    geofacet::facet_geo( ~ state, 
                       grid = "us_state_grid1",
                       scales = "free_y")  +
  
    ggplot2::labs(title = "US positive COVID tests (7-day rolling average)", 
                  subtitle = paste0("Between ", 
                                    min(PosMapCovus$date), 
                                    " and ", 
                                    max(PosMapCovus$date)),
                  y = "New Positive Tests",
                  x = "Days Elapsed") + 
  
  ggplot2::theme(axis.text.x = element_text(angle = 315)) + 

  ggplot2::theme_bw()

geofacet_pos

ggsave(plot = geofacet_pos, 
       filename = "figs/geofacet_pos.png", 
       device = "png", 
       dpi = "retina",
       width = 16, 
       height = 10, 
       units = "in", 
       limitsize = FALSE)

US Cases (NYT vs COVID tracking project)

I want to compare the NYT and COVID-19 tracking datasets (positive tests vs. NYT cases). This can be accomplished using the TidyCovCaseData which has both cases from the NYT dataset, and the positive measure from the COVID tracking project dataset.

geofacet_cases <- TidyCovCaseData %>% 
        ggplot2::ggplot(aes(x = days_elapsed, 
                                     y = `Cases Value`, 
                                     group = `Cases Key`,
                                     color = `Cases Key`)) +
    ggplot2::geom_line(show.legend = TRUE) +
    geofacet::facet_geo( ~ state, 
                       grid = "us_state_grid1",
                       scales = "free_y")  +
  
    ggplot2::labs(title = "US COVID Cases (NYT vs. COVID tracking)", 
                  subtitle = paste0("Between ", 
                                    min(TidyCovCaseData$date), 
                                    " and ", 
                                    max(TidyCovCaseData$date)),
                  y = "New Cases",
                  x = "Days Elapsed") + 
  
  ggplot2::theme(axis.text.x = element_text(angle = 315)) + 
  
  ggplot2::theme_minimal()

geofacet_cases

ggsave(plot = geofacet_cases, 
       filename = "figs/geofacet_cases.png", 
       device = "png", 
       dpi = "retina",
       width = 16, 
       height = 10, 
       units = "in", 
       limitsize = FALSE)

US Deaths (NYT vs COVID tracking project)

Finally, we can repeat this process, but compare deaths between the NYT and COVID-tracking datasets (which later we can turn into a selectInput).

geofacet_deaths <- TidyCovDeathData %>% 
      ggplot2::ggplot(aes(x = days_elapsed, 
                          y = `Death Value`, 
                          group = `Death Key`,
                          color = `Death Key`)) +
    ggplot2::geom_line(show.legend = TRUE) +
    geofacet::facet_geo( ~ state, 
                       grid = "us_state_grid1",
                       scales = "free_y")  +
  
    ggplot2::labs(title = "US COVID deaths (NYT vs. COVID tracking)", 
                  subtitle = paste0("Between ", 
                                    min(DeathsMapCovus$date), 
                                    " and ", 
                                    max(DeathsMapCovus$date)),
                  y = "New Positive Tests",
                  x = "Days Elapsed") + 
  
  ggplot2::theme(axis.text.x = element_text(angle = 315)) + 
  
  ggplot2::theme_minimal()

geofacet_deaths

ggsave(plot = geofacet_deaths, 
       filename = "figs/geofacet_deaths.png", 
       device = "png", 
       dpi = "retina",
       width = 16, 
       height = 10, 
       units = "in", 
       limitsize = FALSE)

Covid - CA flexdashboard (documentation)